Main Article Content

Abstract

In today’s world, bulk of emails is received by every individual out of which many fraudulent or spam emails are also present. The task of a good email service provider is to create an algorithm so that such fraudulent or spam messages are automatically detected and then they are sent to the spam folder. In this paper, the authors proposed a novel technique by which this sorting of email can be done automatically. Using machine learning method, the authors implemented a method in which spam mail and fraudulent messages have been successfully detected and those mails have been sent to the spam folder of the mailbox. The authors, in this paper, presented the description of the algorithm along with the test results.  

Keywords

E-mail classification Machine learning algorithms classifier Naïve-byes

Article Details

Author Biographies

Koustav Pal, Amity University Kolkata

B. Tech student of Electronics and Communication Engineering Department.

Kalyan Chatterjee, Amity University Kolkata

Assistant Professor in the Department of Electronics and Communication Engineering

Sayanti Banerjee, Amity University Kolkata

Assistant Professor in the Department of Electronics and Communication Engineering

Citations
Mondal, S. A., Pal, K., Chatterjee, K., & Banerjee, S. (2020). Spam E-mail classification using Machine Learning techniques. [email protected] - Preprint Archive, 1(1). https://doi.org/10.36375/prepare_u.a66

References

  1. M. N. Marsono, M. W. El-Kharashi, and F. Gebali, “Binary LNS-based naïve Bayes inference engine for spam control: Noise analysis and FPGA synthesis”, IET Computers & Digital Techniques, 2008
  2. Muhammad N. Marsono, M. Watheq El-Kharashi, Fayez Gebali “Targeting spam control on middleboxes: Spam detection based on layer-3 e-mail content classification” Elsevier Computer Networks, 2009
  3. Yuchun Tang, Sven Krasser, Yuanchen He, Weilai Yang, Dmitri Alperovitch ”Support Vector Machines and Random Forests Modeling for Spam Senders Behavior Analysis” IEEE GLOBECOM, 2008 International Journal of Computer Science & Information Technology (IJCSIT), Vol 3, No 1, Feb 2011 184
  4. Guzella, T. S. and Caminhas, W. M. ”A review of machine learning approaches to Spam filtering.” Expert Syst. Appl., 2009
  5. Wu, C. ”Behavior-based spam detection using a hybrid method of rule-based techniques and neural networks” Expert Syst., 2009
  6. Khorsi. “An overview of content-based spam filtering techniques”, Informatica, 2007
  7. Hao Zhang, Alexander C. Berg, Michael Maire, and Jitendra Malic. "SVM-KNN: Discriminative nearest neighbour classification for visual category recognition", IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2006
  8. Carpinteiro, O. A. S., Lima, I., Assis, J. M. C., de Souza, A. C. Z., Moreira, E. M., & Pinheiro, C. A. M. "A neural model in anti-spam systems.", Lecture notes in computer science.Berlin, Springer, 2006
  9. El-Sayed M. El-Alfy, Radwan E. Abdel-Aal "Using GMDH-based networks for improved spam detection and email feature analysis"Applied Soft Computing, Volume 11, Issue 1, January 2011
  10. Li, K. and Zhong, Z., “Fast statistical spam filter by approximate classifications”, In Proceedings of the Joint international Conference on Measurement and Modeling of Computer Systems. Saint Malo, France, 2006
  11. Cormack, Gordon. Smucker, Mark. Clarke, Charles " Efficient and effective spam filtering and re-ranking for large web datasets" Information Retrieval, Springer Netherlands. January 2011
  12. Almeida,tiago. Almeida, Jurandy.Yamakami, Akebo " Spam filtering: how the dimensionality reduction affects the accuracy of Naive Bayes classifiers" Journal of Internet Services and Applications, Springer London , February 2011